Developing an intelligent ICT system for environmentally optimized irrigation management in agriculture

Globally, agriculture makes use of 70% of all water withdrawn from aquifers, streams and lakes. Agriculture accounts for 22% of freshwater abstraction in Europe, outnumbered only by freshwater abstraction for cooling in energy production (45%). In the light of the real need to practically improve the environmental performance of irrigation systems and prevent the misuse of water, the ENORASIS project was established aimed to develop an intelligent, integrated Decision Support System (ENORASIS Service Platform and Components) for environmentally optimized and thus, sustainable irrigation management to be used by farmers and water management organizations. To achieve so, the ENORASIS project developed and integrated a bouquet of advanced technologies, methodologies and models in the fields of: (i) weather prediction systems that exploit satellite observations; (ii) irrigation optimization techniques and (iii) smart irrigation systems; and (iv) wireless sensor networks (functioning with solar energy) as key enabling technology for field measurements and monitoring conditions.The system was tested in 5 pilot locations in Poland, Serbia, Cyprus and Turkey, providing large savings in water use as comparted to other irrigation practices while not compromising crop yields. The highest savings were observed in raspberry and potato plantations in Poland, reaching 90%and 59% respectively

Implementing big data lake for heterogeneous data sources

Abstract Modern connected cities are more and more leveraging advances in ICT to improve their services and the quality of life of their inhabitants. The data generated from different sources, such as environmental sensors, social networking platforms, traffic counters, are harnessed to achieve these end goals. However, collecting, integrating, and analyzing all the heterogeneous data sources available from the cities is a challenge. This article suggests a data lake approach built on Big Data technologies, to gather all the data together for further analysis. The platform, described here, enables data collection, storage, integration, and further analysis and visualization of the results. This solution is the first attempt to integrate a diverse set of data sources from four pilot cities as part of the CUTLER project (Coastal urban development through the lenses of resiliency). The design and implementation details, as well as usage scenarios are presented in this paper

hackAIR: Towards Raising Awareness about Air Quality in Europe by Developing a Collective Online Platform

Although air pollution is one of the most significant environmental factors posing a threat to human health worldwide, air quality data are scarce or not easily accessible in most European countries. The current work aims to develop a centralized air quality data hub that enables citizens to contribute to air quality monitoring. In this work, data from official air quality monitoring stations are combined with air pollution estimates from sky-depicting photos and from low-cost sensing devices that citizens build on their own so that citizens receive improved information about the quality of the air they breathe. Additionally, a data fusion algorithm merges air quality information from various sources to provide information in areas where no air quality measurements exist